Many organizations analyze data in order to learn from, evaluate, and identify information about products, people, places, and the like, which relate to their organization. In order to better understand and analyze the data, it is often modified or enriched. Semantic enrichment refers to the enrichment of content/context of the data and may include tagging data, categorizing data, classifying data, and the like, in relationship to each other, based on dictionaries and other base reference sources. Furthermore, modification of data may include adding additional contextual information to an existing data set (e.g., adding traffic data to road maps to provide context of road conditions, probability of delay, length of projected obstructions, condition of road, etc.) or providing supplemental data to better learn from the existing data. In some cases, a system may automatically modify and/or enrich its understanding of the context and content of data by comparing the data to an existing knowledge store and then build upon that store.
Data enrichment may provide a number of advantages to an organization such as a further understanding of the context of their data enabling more intelligent responses to be generated, the ability to create proactive systems and responses based on aggregated data, an increased accuracy in projections and trends as a result of an enhanced understanding of the context of data from multiple data sources, enhanced reaction times based on evolving understanding of the context, and the like. However, in order to declare a semantic enrichment, an Information Technology (IT) tool is typically required which is controlled by an IT administrator and which is typically hidden from end-users. As a result, an end user is not able to perform semantic enrichment or is very limited in the types of enrichment they can perform. Furthermore, when a user wants to create a new calculated data measure from existing calculated data measures, the user is typically not aware of their options for creating a new measure due to semantic information not being surfaced. That is, knowledge of underlying semantic information is needed prior to calculating a new measure, but in most cases, such information is not user friendly or in a format that is capable of being understood by an end user.
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